Many social networks have become very large and widely used and are increasingly leveraged for information, including characteristics of users, such as for placement of advertising for products or services. Social networks uniquely represent relationships and activities among individuals, communities, and organizations and access to social networks is pervasive in many segments of society as well as available on most computing devices. Accordingly, social networks are generally effective platforms for supporting such advertising.
However, methods for profiling, classifying, and/or modeling social networks, as well as discovering and analyzing topologies represented therein, are often ineffective and/or computationally expensive for very large and/or dynamic networks, requiring traversal of the entire network to analyze each new query regarding network members and/or topologies. Additionally, social network topology analysis often utilizes private or semi-private information, which is not desirable, and/or results in recommendations, such as for placement of advertising, that is uninformed and/or broader than necessary to achieve similar outcomes.